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Modelling the temperature of Electric Vehicle (EV) batteries is a fundamentaltask of EV manufacturing. Extreme temperatures in the battery packs can affecttheir longevity and power output. Although theoretical models exist fordescribing heat transfer in battery packs, they are computationally expensiveto simulate. Furthermore, it is difficult to acquire data measurements fromwithin the battery cell. In this work, we propose a data-driven surrogate model(LiFe-net) that uses readily accessible driving diagnostics for batterytemperature estimation to overcome these limitations. This model incorporatesNeural Operators with a traditional numerical integration scheme to estimatethe temperature evolution. Moreover, we propose two further variations of thebaseline model: LiFe-net trained with a regulariser and LiFe-net trained withtime stability loss. We compared these models in terms of generalization erroron test data. The results showed that LiFe-net trained with time stability lossoutperforms the other two models and can estimate the temperature evolution onunseen data with a relative error of 2.77 % on average.

LiFe-net: Data-driven Modelling of Time-dependent Temperatures and Charging Statistics Of Tesla's LiFePo4 EV Battery